





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class  _UpperCAmelCase				(		_lowerCamelCase      ,							unittest.TestCase  ):
      a    							=				KandinskyInpaintPipeline
      a    							=				["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a    							=				[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a    							=				[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a    							=				False
      @property
      def 		_lowerCamelCase   (		self							):
         return 32
      @property
      def 		_lowerCamelCase   (		self							):
         return 32
      @property
      def 		_lowerCamelCase   (		self							):
         return self.time_input_dim
      @property
      def 		_lowerCamelCase   (		self							):
         return self.time_input_dim * 4
      @property
      def 		_lowerCamelCase   (		self							):
         return 100
      @property
      def 		_lowerCamelCase   (		self							):
         A_  :					List[str]         =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
         return tokenizer
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					Tuple         =						MCLIPConfig(
             numDims=self.cross_attention_dim   ,							transformerDimensions=self.text_embedder_hidden_size   ,							hidden_size=self.text_embedder_hidden_size   ,							intermediate_size=37   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							vocab_size=1005   ,							)

         A_  :					List[str]         =						MultilingualCLIP(a__							)
         A_  :					int         =						text_encoder.eval()

         return text_encoder
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)

         A_  :					Optional[int]         =						{
             'in_channels': 9,
             # Out channels is double in channels because predicts mean and variance
             'out_channels': 8,
             'addition_embed_type': 'text_image',
             'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
             'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
             'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
             'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
             'layers_per_block': 1,
             'encoder_hid_dim': self.text_embedder_hidden_size,
             'encoder_hid_dim_type': 'text_image_proj',
             'cross_attention_dim': self.cross_attention_dim,
             'attention_head_dim': 4,
             'resnet_time_scale_shift': 'scale_shift',
             'class_embed_type': None,
         }

         A_  :					Optional[int]         =						UNetaDConditionModel(**a__							)
         return model
      @property
      def 		_lowerCamelCase   (		self							):
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					List[Any]         =						VQModel(**self.dummy_movq_kwargs							)
         return model
      def 		_lowerCamelCase   (		self							):
         A_  :					Tuple         =						self.dummy_text_encoder
         A_  :					List[Any]         =						self.dummy_tokenizer
         A_  :					Union[str, Any]         =						self.dummy_unet
         A_  :					Dict         =						self.dummy_movq

         A_  :					str         =						DDIMScheduler(
             num_train_timesteps=1000   ,							beta_schedule="""linear"""   ,							beta_start=0.0_0085   ,							beta_end=0.012   ,							clip_sample=a__   ,							set_alpha_to_one=a__   ,							steps_offset=1   ,							prediction_type="""epsilon"""   ,							thresholding=a__   ,							)

         A_  :					Tuple         =						{
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components
      def 		_lowerCamelCase   (		self   ,							a__   ,							a__=0							):
         A_  :					Tuple         =						floats_tensor((1, self.cross_attention_dim)   ,							rng=random.Random(a__							)							).to(a__							)
         A_  :					List[Any]         =						floats_tensor((1, self.cross_attention_dim)   ,							rng=random.Random(seed + 1							)							).to(a__							)
         # create init_image
         A_  :					Any         =						floats_tensor((1, 3, 64, 64)   ,							rng=random.Random(a__							)							).to(a__							)
         A_  :					Union[str, Any]         =						image.cpu().permute(0   ,							2   ,							3   ,							1							)[0]
         A_  :					Dict         =						Image.fromarray(np.uinta(a__							)							).convert("""RGB"""							).resize((256, 256)							)
         # create mask
         A_  :					Union[str, Any]         =						np.ones((64, 64)   ,							dtype=np.floataa							)
         A_  :					List[Any]         =						0

         if str(a__							).startswith("""mps"""							):
            A_  :					List[str]         =						torch.manual_seed(a__							)
         else:
            A_  :					Union[str, Any]         =						torch.Generator(device=a__							).manual_seed(a__							)
         A_  :					Any         =						{
             'prompt': 'horse',
             'image': init_image,
             'mask_image': mask,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 64,
             'width': 64,
             'num_inference_steps': 2,
             'guidance_scale': 4.0,
             'output_type': 'np',
         }
         return inputs
      def 		_lowerCamelCase   (		self							):
         A_  :					List[Any]         =						'cpu'

         A_  :					Tuple         =						self.get_dummy_components()

         A_  :					str         =						self.pipeline_class(**a__							)
         A_  :					Dict         =						pipe.to(a__							)

         pipe.set_progress_bar_config(disable=a__							)

         A_  :					Tuple         =						pipe(**self.get_dummy_inputs(a__							)							)
         A_  :					str         =						output.images

         A_  :					Optional[int]         =						pipe(
             **self.get_dummy_inputs(a__							)   ,							return_dict=a__   ,							)[0]

         A_  :					Optional[Any]         =						image[0, -3:, -3:, -1]
         A_  :					str         =						image_from_tuple[0, -3:, -3:, -1]

         print(F"""image.shape {image.shape}"""							)

         assert image.shape == (1, 64, 64, 3)

         A_  :					int         =						np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]							)

         assert (
             np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
         ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
         ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






      def 		_lowerCamelCase   (		self							):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3							)






@slow
@require_torch_gpu
class  _UpperCAmelCase				(		unittest.TestCase  ):
      def 		_lowerCamelCase   (		self							):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






      def 		_lowerCamelCase   (		self							):
         A_  :					Tuple         =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""							)

         A_  :					List[str]         =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
         A_  :					Dict         =						np.ones((768, 768)   ,							dtype=np.floataa							)
         A_  :					Union[str, Any]         =						0

         A_  :					Optional[Any]         =						'a hat'

         A_  :					Optional[int]         =						KandinskyPriorPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1-prior"""   ,							torch_dtype=torch.floataa							)
         pipe_prior.to(a__							)

         A_  :					Optional[int]         =						KandinskyInpaintPipeline.from_pretrained(
             """kandinsky-community/kandinsky-2-1-inpaint"""   ,							torch_dtype=torch.floataa							)
         A_  :					int         =						pipeline.to(a__							)
         pipeline.set_progress_bar_config(disable=a__							)

         A_  :					List[str]         =						torch.Generator(device="""cpu"""							).manual_seed(0							)
         A_  :					Any         =						pipe_prior(
             a__   ,							generator=a__   ,							num_inference_steps=5   ,							negative_prompt=""""""   ,							).to_tuple()

         A_  :					int         =						pipeline(
             a__   ,							image=a__   ,							mask_image=a__   ,							image_embeds=a__   ,							negative_image_embeds=a__   ,							generator=a__   ,							num_inference_steps=100   ,							height=768   ,							width=768   ,							output_type="""np"""   ,							)

         A_  :					Any         =						output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(a__   ,							a__							)














"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)